Unsupervised domain adaptation (UDA) attempt to mitigate the burden of annotation by using large amounts of synthetic data generated with known labels (e.g.using a graphics engine) as a source domain (e.g.GTA5 [1]} and SYNTHIA [2]}) and unlabeled examples of a target domain. The goal of UDA is to transfer learned knowledge from a labeled source domain to an unlabeled target domain by bridging the domain shift between them. Recent UDA methods have achieved significant improvements by employing different methods such as self-training [3]}, [4]}, [5]}, [6]}, [7]}, generative models [8]}, [9]}, adversarial learning [10]}, [11]} and entropy minimization [12]}, [13]}. However, due to not fully capturing information from target domain data, there is still a large performance gap between such solutions and their fully-supervised counterparts.
